Method of non-targeted complex sample analysis

ABSTRACT

A method for non-targeted complex sample analysis which involves the following steps. A first step involves providing a database containing identifying data of known molecules. A second step involves introducing a complex sample containing multiple unidentified molecules into a Fourier Transform Ion Cyclotron Mass Spectrometer to obtain data regarding the molecules in the complex sample with the identifying data of known molecules in order to arrive at an identification through comparison of the molecules in the sample.

This application is a continuation of U.S. patent application Ser. No.10/208,276, filed Jul. 30, 2002, which is a national stage applicationunder 35 U.S.C. § 371 of PCT Application No. PCT/CA01/00111, filed Feb.1, 2001, which claims priority benefit of Canadian Patent ApplicationNo. 2,298,181, filed Feb. 2, 2000.

FIELD OF THE INVENTION

The present invention relates to a method of non-targeted complex sampleanalysis, with particular application to biology, and genomics inparticular.

BACKGROUND OF THE INVENTION

Functional genomics is an emerging field in biotechnology that focuseson the characterization of gene function. All organisms contain only onegenotype. However, the expression of this genotype under varyingdevelopmental and environmental conditions results in an almost infinitenumber of possible phenotypes. It is the correlation of gene expressionto phenotype that defines functional genomics. To properly study a genewe need to not only know its identity (i.e. sequence) but to be able toobserve and characterize its expression patterns in response todevelopmental and environmental changes, in isolation as well as inrelation to the other genes in the genome. To properly study the effectsresulting from the expression of a gene we need to be able tocharacterize the phenotype resulting from this activity in an objectiveand quantifiable manner. This is what the non-targeted metabolicprofiling technology invention described herein enables the functionalgenomics community to do.

The gene sequences of entire species are now known. Gene-chip technologyhas made it possible to monitor and quantify the changes in expressionof each and every gene within the genome to developmental andenvironmental changes, simultaneously. Gene-chip technology is, inessence, non-targeted gene expression analysis even though it is, inactuality, a targeted analysis that just so happens to contain all ofthe possible targets. This is a powerful comprehensive capability, butit was made possible by the fact that the genome is a finite and unitaryentity. The analogous phenotypic capability would be to have everymetabolite and protein of an organism known and on a chip. This is notpossible due to the fact that not only are there multiple phenotypes,but a virtually infinite number of metabolites and proteins arepossible. To be complementary to the current state of genomic analysis,phenotypic analysis must be non-targeted in “actuality”. Thenon-targeted metabolic profiling technology described herein is the onlyplatform that satisfies the requirements of non-targeted phenotypicanalysis. Furthermore, this technology is not restricted to any onespecies, but is equally effective in all plant and animal species.

Deciphering the complex molecular makeup of an individual phenotype is aformidable task. To be able to accurately and reproducibly generate thisphenotypic information in such a way that the virtually infinite numberof possible phenotypes can be compared to one another and correlated togene expression is the crux of the dilemma that faces functionalgenomics. On the molecular level, the phenotype of a given biologicalsystem can be divided into the proteome and the metabolome. Since geneexpression results in protein synthesis, the proteome is the first andmost direct link to gene expression. However, due to the complexinteractions of metabolic pathways, it is difficult to predict theeffects that changes in the expression levels of a given protein willhave on the overall cellular processes that it may be involved in. Themetabolome, on the other hand, is the summation of all metabolic(proteomic) activities occurring in an organism at any given point intime. The metabolome is therefore a direct measure of the overall or endeffect of gene expression on the cellular processes of any givenbiological system at any given time. For this reason, the metabolomeshould prove to be the more powerful of the two phenotypes in actuallyunderstanding the effects of gene function and manipulation. Thenon-targeted metabolic profiling technology described herein is the onlycomprehensive metabolic profiling technology available.

Isolation, identification, and quantitation are the three fundamentalrequirements of all analytical methods. The primary challenge for anon-targeted metabolome analysis is to meet these requirements for allof the metabolites in the metabolome, simultaneously. The second andperhaps more difficult challenge is to be able to meet theserequirements with sufficient throughput and long-term stability suchthat it can be used side by side with gene-chip technology. Suchtechnology will drastically reduce the time that is required for thefunction of a particular gene to be elucidated. In addition, databasesof such analyses enable very large numbers of phenotypes and genotypesto be objectively and quantitatively compared. There is no such productor technology available to functional genomics scientists at this time.The non-targeted metabolic profiling technology described herein hasbeen extensively tested in multiple species. In all cases, thetechnology has verified the metabolic variations known to exist betweenvarious genotypes and developmental stages of different species.

Key Technology Concept. The non-targeted metabolic profiling technologydescribed herein can separate, quantify and identify all of thecomponents in a complex biological sample quickly and simultaneously.This is achieved without any a priori selection of the metabolites ofinterest and is therefore unbiased. These data are exported to adatabase that allows the researcher to directly compare one sample toanother (i.e. mutant vs. wild-type, flowering vs. stem elongation,drought stress vs. normal growing conditions, etc.) or to organize theentire database by metabolite concentration (i.e. which genotype has thegreatest or least expression of a given metabolite). This technology isequally applicable to the study of human disease. To make use of thisinformation, the researcher just types in the empirical formula (s) orthe accurate mass(es) of the metabolite(s) he or she is interested inand the software will organize the data accordingly.

The ability to conduct an analysis of the composition of substances inbiological samples is critical to many aspects of health care,environmental monitoring as well as the product development process.Typically the amount of a specific substance in a complex mixture isdetermined by various means. For example, in order to measure analytesin a complex mixture, the analyte(s) of interest must be separated fromall of the other molecules in the mixture and then independentlymeasured and identified.

In order to separate the analytes in a complex mixture from one another,unique chemical and/or physical characteristics of each analyte are usedby the researcher to resolve the analytes from one another. These uniquecharacteristics are also used to identify the analytes. In allpreviously published reports of complex mixture analysis, themethodologies require known analytical standards of each potentialanalyte before the presence and/or identity of a component in theunknown sample can be determined. The analytical standard(s) and theunknown sample(s) are processed in an identical manner through themethod and the resulting characteristics of these standards recorded(for example: chromatographic retention time). Using this information, asample containing unknown components can be analyzed and if a componentin the unknown sample displays the same characteristic as one of theknown analytical standard (s), the component is postulated to be thesame entity as the analytical standard. This is targeted analysistechnology. Targeted analysis technology is one-way. The researcher cango from known standard to methodology characteristics but not frommethodology characteristics to known standard. The researcher can onlyconfirm or refute the presence and/or amount of one of the previouslyanalyzed standards. The researcher cannot go from the methodcharacteristics of an unknown analyte to its chemical identity. Themajor drawback of this type of analysis is that any molecule that wasnot identified prior to analysis is not measured. As a result, muchpotentially useful information is lost to the researcher. To be trulynon-targeted, the method must allow the researcher to equally evaluateall of the components of the mixture, whether they are known or unknown.This is only possible if the defining physical and/or chemicalcharacteristics of the analyte are not related to the method of analysisbut are inherent in the composition of the analyte itself (i.e. itsatomic composition and therefore its accurate mass).

Key Benefits of Non-Targeted Metabolic Profiling Technology

1. Multidisciplinary. Virtually only one set of analyses would need tobe performed on a given sample and the data resulting from this analysiswould be available to all scientists regardless of the area of researchthey are focusing on.2. Comprehensive. The non-targeted approach assesses ALL metabolitechanges and will thus lead to a faster and more accurate determinationof gene function/disfunction.3. Unknown Metabolite Discovery. The non-targeted approach has thepotential of identifying key metabolic regulators that are currentlyunknown, and which would not be monitored in a targeted analysisscenario.4. High Throughput. The system is can be fully automated and analysistime is short allowing 100's of samples to be analyzed per instrumentper day.5. Quantitative. The system is reproducible and has an effective dynamicrange >104. Relative changes in metabolite expression over entirepopulations can be studied.

Business Impact of Technology. The ability to generate searchabledatabases of the metabolic profiles of a given organism will represent arevolution in how the effects of genetic manipulation on a species canbe studied. Currently our knowledge of the actual genetic code is muchgreater that our knowledge of the functions of the genes making up thiscode. After the mapping of the genome, the next greatest challenge willbe determining the function and purpose of these gene products and howmanipulation of these genes and their expression can be achieved toserve any number of purposes. The time, energy, and cost ofinvestigating the effects of genetic manipulation are great. A databasethat can be searched for multiple purposes and which contains directmeasures of the metabolic profiles of specific genotypes has thepotential to dramatically decrease the amount of time required todetermine the function of particular gene products. Such a database willreduce the risk of investing a large amount of time and resourcesresearching genes which may have effects on protein expression, but dueto down-stream feedback mechanisms, no net effect on metabolism at thewhole cell or organism level.

In an article published in CURRENT OPINION IN PLANT BIOLOGY in 1999entitled “Metabolic Profiling: a Rosetta Stone for genomics?”,Trethewey, Krotzky and Willmitzer indicated that exponentialdevelopments in computing have opened up the “possibility” of conductingnon-targeted experimental science. While recognizing that it would notbe possible to work with infinite degrees of freedom, the opinion wasadvanced that the power of post-experimental data processing would makepossible this non-targeted approach. The non-targeted approach describedin that article dealt only with the post acquisition analysis ofmetabolite data; not the non-targeted collection of metabolite data.

Thus the feasibility of non-targeted analysis of complex mixtures isneither obvious nor simple. The three major problems surrounding thenon-targeted analysis of complex mixtures are: the ability to separateand identify all of the components in the mixture; the ability toorganize the large amounts of data generated from the analysis into aformat that can be used for research; and the ability to acquire thisdata in an automated fashion and in a reasonable amount of time.

SUMMARY OF THE INVENTION

What is required is a method of non-targeted complex sample analysis.

According to the present invention there is provided a method fornon-targeted complex sample analysis that involves the following steps.A first step involves providing a database containing identifying dataof known molecules (this database contains the elemental compositions ofall molecules previously identified in nature, organized by species,metabolic processes, subcellular location, etc.). A second step involvesintroducing a complex sample containing multiple unidentified moleculesinto a Fourier Transform Ion Cyclotron Mass Spectrometer to obtain dataregarding the molecules in the complex sample. A third step involvescomparing the collected data regarding the molecules in the complexsample with the identifying data of known molecules in order to arriveat an identification through comparison of the molecules in the sample.Molecules that are not represented in the database (i.e. unknowns) areautomatically identified by determining their empirical formula. Thus,the method allows rapid identification of new molecules within thecomplex mixture related to specific molecules already identified, aswell as identification of those molecules within the complex mixturethat bear no relationship to those class or category of moleculesalready defined. As a result the analysis of complex mixtures is greatlysimplified.

The invention, as described, uses the high resolving power of FourierTransform Ion Cyclotron Mass Spectrometry (FTMS) to separate all of thecomponents within the mixture that have different empirical formulas.This has been shown for petroleum distillates, but not for aqueousbiological samples ionized in a “soft” ionization mode, where adductions can be problematic. The accurate mass capability of FTMS thatenables the determination of empirical formula has been widelyestablished. Furthermore FTMS is capable of performing highresolution/accurate mass 2D MS/MS which provides structural informationthat can be used to confirm the identities of components that haveidentical empirical formulas and allows the organization of metabolitesbased upon common structural components. This capability has been shownby isolated research groups but is not available on a commercialinstrument. By integrating these capabilities with an automated sampleinjection system and an automated data integration and database system,all of the components within a complex mixture can be analyzed rapidlyand simultaneously. The data is then exported into a database that canbe searched and organized by sample, or analyte. It is to be noted thatunlike the approach advocated by Trethewey, Krotzky and Willmitzer, thepresent method is not dependant upon the advances in post experimentaldata processing. The non-targeted metabolic profiling technologydescribed herein generates a dataset that is simple and compact.Computing technology capable of organizing and interpreting thedescribed databases is readily available. No new advances are required.Furthermore, the technology does not have the finite limits inherent inthe approach of Trethewey, Krotzky and Willmitzer.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of the invention will become more apparent fromthe following description in which reference is made to the appendeddrawings and figures, the drawings and figures are for the purpose ofillustration only and are not intended to in any way limit the scope ofthe invention to the particular embodiment or embodiments shown,wherein:

FIG. 1 is a side elevation view depicting non-targeted analysis ofcomplex samples in accordance with the teachings of the presentinvention.

FIG. 2 is an illustration of raw data (mass spectrum) collected from theFTMS showing how the metabolites in the complex mixture are separatedfrom one another. Mass range displayed 100-350 amu.

FIG. 3 is an illustration of raw data (mass spectrum) collected from theFTMS showing how the metabolites in the complex mixture are separatedfrom one another. 10 amu mass range displayed.

FIG. 4 is an illustration of raw data (mass spectrum) collected from theFTMS showing how the metabolites in the complex mixture are separatedfrom one another. 1 amu mass range displayed.

FIG. 5 is an illustration of raw data (mass spectrum) collected from theFTMS showing how the metabolites in the complex mixture are separatedfrom one another. Mass range displayed 100-350 amu. 0.1 amu window.

FIG. 6 is an illustration of strawberry pigment pathway (comparison ofdifferent developmental stages of an organism).

FIG. 7 is an illustration of the extracted mass spectra of Phenylalaninefrom strawberry extracts from different developmental stages.

FIG. 8 is an illustration of the extracted mass spectra of Cinnamatefrom strawberry extracts from different developmental stages.

FIG. 9 is an illustration of the extracted mass spectra of 4-Coumaratefrom strawberry extracts from different developmental stages.

FIG. 10 is an illustration of the extracted mass spectra of Naringeninfrom strawberry extracts from different developmental stages.

FIG. 11 is an illustration of the extracted mass spectra of Pelargonidinfrom strawberry extracts from different developmental stages.

FIG. 12 is an illustration of the extracted mass spectra ofPelargonidin-3-glucoside from strawberry extracts from differentdevelopmental stages.

FIG. 13 is an illustration of glucosinolate mutants in Arabidopsisthaliana (comparison of genetic mutants to wild-type and identificationof unknown metabolites). Relative changes in 3-MethylthiobutylGlucosinolate illustrated.

FIG. 14 is an illustration of glucosinolate mutants in Arabidopsisthaliana (comparison of genetic mutants to wild-type and identificationof unknown metabolites). Relative changes in 3-MethylsulphinylpropylGlucosinolate illustrated.

FIG. 15 is an illustration of glucosinolate mutants in Arabidopsisthaliana (comparison of genetic mutants to wild-type and identificationof unknown metabolites). Relative changes in 3-MethylsulphinylheptylGlucosinolate illustrated.

FIG. 16 is an illustration of Tobacco Flower Analysis (Location ofmetabolite expected to be responsible for red color in tobacco).

FIG. 17 is an illustration of Tobacco Flower Analysis (Location ofunknown metabolite potentially involved in tobacco color).

FIG. 18 is an illustration of Observed Metabolic Changes in StrawberryDevelopment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The preferred method of non-targeted complex sample analysis embodimentwill now be described with reference to FIG. 1 The purpose of thisinvention is to provide a means of analyzing large numbers of complexsamples, for example biological extracts, and be able to analyze theinformation in a non-targeted fashion after the analysis is complete todetermine the differences between samples.

In the invention complex samples are directly injected into the FTMS 12though the use of an autosampler 14 with or without the additional useof a chromatographic column. The components of the mixture are ionizedby one of many potential “soft” ionization sources (electrospray, APCI,FAB, SIMS, MALDI, etc.) and then transferred into the ion cyclotronresonance (ICR) cell with or without additional mass-selectivepre-separation (quadrupole, hexapole, etc.). The ions are then separatedand measured in the ICR cell with or without simultaneous MS/MSoccurring. The data collected (mass spectrum) is integrated (the mass,relative intensity, absolute intensity of each ion is determined) andprocessed, with or without calibration with known molecules of knownconcentrations. These data, with or without isotope elimination andempirical formula calculation, are then transferred to a database 16that organizes and stores the data for future comparisons and functionalanalyses. Once stored in the database, individual samples can becompared with one another and those molecules that show differentconcentrations between the selected samples can be displayed. The entiredatabase can be searched for specific molecules. The samples in thedatabase can be listed from highest to lowest concentration orvice-versa. The molecules detected in the analysis can be compared witha database of known molecules and the molecules automaticallyidentified. For molecules that do not match known molecules, the mostlikely empirical formulas can be displayed.

This approach provides numerous advantages to the researcher. There is adramatic increase in the amount of information obtained from each sample(>10× compared to the most comprehensive targeted analysis procedurereported). Information is collected on both known and unknown componentsof a mixture. There is increased efficiency of data collection (datacollection is approximately 10× faster than reported targeted analysistechniques). It provides a basis for unbiased comparison of unknownsamples. Effects of gene modification on total cell metabolism can bedetermined instead of effects on only a small subset of metabolicprocesses (i.e. the relationship between different metabolic processescan be studied). By analyzing all metabolites the actual step within ametabolic process that is disrupted can be determined. Genemodifications that have an effect on protein expression but no neteffect on cell metabolism can be identified. All of these analyses arecompleted simultaneously in one fast analysis, whereas multipletime-consuming analyses would have to be performed to get identical dataat a tremendously higher cost.

Many examples exist for the use of FTMS for the analysis of complexmixtures, but none have introduced the concept of non-targeted analysisfollowed by database formation. The described method recognizes andutilizes some heretofore unused capabilities in FTMS. FTMS has thetheoretical resolving power to separate all of the metabolites ofdifferent empirical formula in a complex biological sample. FTMS has thetheoretical accurate mass capabilities to assign empirical formulas toall of the metabolites in the complex biological sample. FTMS has thecapability to perform 2 dimensional MS/MS on all of the metabolites in acomplex biological sample. It is not necessary to know a priori whatmetabolites are present in a complex biological sample if the analytescould thus be separated and then be identified based upon theirempirical formula and MS/MS fragment data and or by comparing them to adatabase of known analytes. Complex samples can be compared with oneanother to determine what analytes had different intensities between thesamples. A database could be organized by analyte or by common MS/MSfragments. This approach significantly decreases the time and resourcesneeded to elucidate gene function as a result of genetic manipulation,environmental changes, or developmental changes in an organism. One ofthe many applications of the described method invention include genefunction determination in functional genomics research.

Numerous targeted LC-MS methods as well as other screening methods havebeen developed to analyze specific molecules or groups of molecules incomplex samples. The major reason that this invention is novel and notobvious is because it employs a fundamentally different strategy foranalytical analysis and is only possible with highly specializedinstrumentation and methodology. Although the many independenttheoretical research capabilities of FTMS have been known for at least10 years, FTMS has only been used in a targeted way and for specializedresearch purposes. In the past 10 years no group has described theapplication of FTMS employed within the scope of the present invention.The present invention involves the combining of several theoretical FTMScapabilities into a comprehensive, non-targeted metabolic profilingprocedure that has commercial utility in the analysis and interpretationof complex mixtures.

The method of the present invention comprises the following steps:

Generation of Known Metabolite Database. The identity (common name andempirical formula) and relevant biological information (species,metabolic processes involved in, cellular and subcellular location, etc)of all known biological metabolites are inputted into a commercialdatabase program (i.e. Microsoft EXCEL, Table I.). The accuratemonoisotopic mass of these metabolites is automatically determined alongwith their [M+H]+ and [M−H]− accurate mass (M+H and M−H refer to themass of the metabolite when a proton (H+) is either added to themetabolite to create a positively charged ion or removed from themetabolite to create a negatively charged metabolite). The datacollected from the FTMS analysis of the complex sample can then becompared to this database to immediately identify many of the componentsin the complex sample.

Preparation of samples for analysis. The metabolites are extracted fromtheir biological source using any number of extraction/clean-upprocedures that are typically used in quantitative analytical chemistry.Procedures are normally tailored to the source of the sample (i.e. leaftissue, root tissue, blood, urine, brain, etc). For example, a 0.1 gplant leaf sample may be extracted by placing it, 1.0 ml of 50/50MeOH/0.1% formic acid, and 3 small glass beads in a test tube and thenvortexing for one minute to homogenize the sample. The test tube is thencentrifuged for 5 minutes. 100 ul of the supernatant is then transferredfrom the test tube to a 96 well plate. The 96 well plate is placed uponthe autosampler. 20 ul of the supernatant is injected into the FTMS.

Typical Operating Conditions

Solvents. 50/50 MeOH/0.1% ammonium hydroxide as the mobile phase and fordilution for all negative ionization analyses and 50/50 MeOH/0.1% formicacid for all positive ion analyses.

Instrumentation. Bruker Daltonics APEX III Fourier Transform MassSpectrometer (FTMS) equipped with a 7.0 Tesla actively shielded superconducting magnet with electrospray (ESI) and atmospheric chemicalionization (APCI) sources. ESI, APCI, and ion transfer conditions wereoptimized for sensitivity and resolution using a standard mix of serine,tetra-alanine, reserpine, HP Mix, and adrenocorticotrophic hormonefragment 4-10. Instrument conditions were optimized for ion intensityand broadband accumulation over the mass range of 100-1000 amu. Onemegaword data files were acquired and a sinm data transformation wasperformed prior to Fourier transform and magnitude calculations.

Calibration. All samples were internally calibrated for mass accuracyover the approximate mass range of 100-1000 amu using a mixture of theabove-mentioned standards.

Sample Analysis

Samples are introduced to the FTMS via an autosampler, or in some caseswith a syringe pump. When the sample solution reaches the source of theFTMS (the source is where the FTMS ionizes the molecules in the samplesolution), then molecules are ionized according to the principles of theparticular ionization source used. The source can either be external tothe mass analyzer or internal, depending on the type of ionization (forexample in ESI and APCI ions are generated external to the mass analyzerand then transferred to the mass analyzer, whereas in electron impactionization the molecules are ionized internal to the mass analyzer). Theions once generated and transferred (if necessary) to the mass analyzerare then separated and detected in the mass analyzer based upon theirmass to charge ratio.

Analyte Detection

All of the analytes within the complex mixture are analyzedsimultaneously (see FIGS. 2-5). Structurally specific information(accurate mass with or without accurate MS/MS fragment masses) isobtained for all of the analytes without prior knowledge of theanalyte's identity, and then this data is formatted in a way that isamicable to a comprehensive database.

Complex Sample Database Formation

The typical process of database formation involves the following steps:

-   -   1. The output of the FTMS (calibrated mass spectrum) is filtered        to remove all 13C isotopes and peaks that have mass defects that        do not correspond to singly charged biological metabolites;    -   2. Each of the peaks in this filtered peak list is then analyzed        using the mass analysis program that is part of the instrument        manufacturer's software package according to the elemental        constraints provided by the researcher. This program returns all        of the possible elemental compositions that are possible at a        given mass within a certain selected error range.    -   3. Only the data (file name, sample ID, mass, relative        intensity, absolute intensity, empirical formula (s)) from those        peaks in the filtered peak list that satisfied the above        constraints are exported to a final processed data file (Table        II). Each sample analysis results in such a final processed data        file.    -   4. Multiple databases can then be formed from the combining and        comparing of the data files. Three such databases are:        -   a) Direct comparison of two samples to create a database of            differences (Table VI);        -   b) Combination of multiple files to create a database            capable of tracking changes through a series of samples            (Table III);        -   c) Direct comparison of a whole series of samples to one            control sample and then the combination of all the samples            in the series into one database to allow comparisons within            the series vs a common control (FIG. 8).

The utility of the invention is illustrated in the following examples:

I. The Ability to Compare Different Developmental Stages of an Organism(FIGS. 6-12, Table IV).

In this example, we looked at the strawberry pigment pathway instrawberries. FIG. 6 shows the full metabolic pathway. FIGS. 7-12 showthe various metabolites in the pathway that we observed. It is to benoted that we were able to look at molecules of vastly differentchemical compositions (amino acid, acid, flavenoid, glucoside). Here wewere able to see the changes within a single genotype (red strawberry)as a function of developmental stage (green-white-turning-red) andcompare it to a different genotype (white mutant). Only the non-targetedmetabolic profiling technology described herein has this broad of aspectrum. Furthermore, as indicated in Table IV, these changes in themetabolome are directly correlated with changes in gene expression.

II. The Ability to Compare Different Genotypes (FIGS. 13-15, Table V).

In this example three different Arabidopsis thaliana mutants (TU1, TU3,TU5) that are known to have changes in the content and concentration ofglucosinolates were compared to a wild-type (WT). In this instance thenon-targeted metabolic profiling technology described herein was able toconfirm previous results as well as identify glucosinolate changes thathad never before been observed.

III. The Ability to Detect and Identify Unknown Metabolites Involved inKey Pathways (FIGS. 16 and 17, Table IX).

In this example the flowers of a control (red) tobacco was compared to awhite mutant. It was expected that the glucoside (FIG. 16) was themetabolite responsible for color. However, when analyzed by thenon-targeted metabolic profiling method, the expected metabolite was notobserved. An unknown metabolite (FIG. 17) was detected and identified(Table IX) to be the metabolite responsible for tobacco flower color.

IV. The Ability to Compare the Effects of Different EnvironmentalConditions on an Organism (Table VI)

In this example the exuate from a carrot root grown under normal growingconditions (sufficient phosphate) was compared to the exuate from acarrot root grown under abnormal growing conditions (insufficientphosphate). Using non-targeted metabolic profiling we were able toidentify key plant hormones that are excreted to promote symbioticfungal growth under conditions of low phosphate.

V. The Ability to Group and Classify Metabolites Based Upon AccurateMS/MS Data (Table VII and Table VIII)

In this example accurate MS/MS fragmentation data was collected on themetabolites that were observed to be increased in the low phosphateconditions described above. Classes of molecules that have a similarsubstructure can be grouped together (in this case all metabolites withthe C10H9N6O2 fragment). This capability greatly enhances the ability tosearch and characterize different complex mixtures

VI. The Ability to Comprehensively Monitor the Metabolites of anOrganism (Table X, FIG. 18)

In our study of the developmental stages of strawberry, we characterizedthe number of metabolites that we were observed as well as the number ofmetabolites that were observed to have changed in concentration betweenthe different developmental stages. It is the comprehensive nature ofthis method that allows one to monitor and evaluate virtually allongoing metabolic processes independently or in relation to one another.No other technology has this capability.

TABLE I Example of Known Metabolite Database Common MetabolicMonoisotopic Masses Name Process Abbrev. C H N O P S M M + H M − Hglyoxylate 2 2 3 74.0004 75.0076 72.9932 Glycine Gly, G 2 5 1 2 75.032076.0392 74.0248 pyruvic acid PA 3 4 3 88.0160 89.0233 87.0088 L-AlanineAla, A 3 7 1 2 89.0477 90.0549 88.0404 Lactic Acid 3 6 3 90.0317 91.038989.0245 Cytosine 3 5 3 1 99.0432 100.0505 98.0360 Acetoacetic acid 4 6 3102.0317 103.0389 101.0245 gamma aminobutylate GABA 4 9 1 2 103.0633104.0705 102.0561 L-serine 3 7 1 3 105.0426 106.0498 104.0354 Histamine5 9 3 111.0796 112.0869 110.0724 Uracil 4 4 2 2 112.0273 113.0345111.0200 3-cyanoalanine 4 6 2 2 114.0429 115.0501 113.0357 L-ProlinePro, P 5 9 1 2 115.0633 116.0705 114.0561 L-Valine Val, V 5 11 1 2117.0790 118.0862 116.0717 succinite 4 6 4 118.0266 119.0338 117.0194L-Homoserine 4 9 1 3 119.0582 120.0655 118.0510 L-Threonine Thr, T 4 9 13 119.0582 120.0655 118.0510 phosphoenolpyruvic acid PEP 3 6 3 1121.0054 122.0127 119.9982 L-cysteine Cys, C 3 7 1 2 1 121.0197 122.0270120.0125 Nicotinic Acid 6 5 1 2 123.0320 124.0392 122.0248 Thymine 5 6 22 126.0429 127.0501 125.0357 L-Isoleueine Ile, I 6 13 1 2 131.0946132.1018 130.0874 L-Leucine Leu, L 6 13 1 2 131.0946 132.1018 130.0874oxiloacetic acid OAA 4 4 5 132.0059 133.0131 130.9986 L-aspargine Asn, N4 8 2 3 132.0535 133.0607 131.0462 L-Ornithine 5 12 2 2 132.0899133.0971 131.0826 L-Aspartate Asp, D 4 7 1 4 133.0375 134.0447 132.0303Ureidoglycine 3 7 3 3 133.0487 134.0559 132.0415 L-malic acid 4 6 5134.0215 135.0287 133.0143 Ureidoglycolate 3 6 2 4 134.0327 135.0400133.0255 L-Homocysteine 4 9 1 2 1 135.0354 136.0426 134.0282 Adenine(Vitamin B4) 5 5 5 135.0545 136.0617 134.0473 Adenine 5 5 5 135.0545136.0617 134.0473 3-Methyleneoxindole Auxins 9 7 1 1 145.0528 146.0600144.0455 Indolealdehyde Auxins 9 7 1 1 145.0528 146.0600 144.0455Indolenine epoxide Auxins 9 7 1 1 145.0528 146.0600 144.0455alpha-Ketoglutarate 5 6 5 146.0215 147.0287 145.0143 L-Glutamine Gln, Q5 10 2 3 146.0691 147.0763 145.0619 L-Lysine Lys, L 6 14 2 2 146.1055147.1127 145.0983 L-Glutamate Glu, E 5 9 1 4 147.0531 148.0604 146.0459L-Methionine Met, M 5 11 1 2 1 149.0510 150.0583 148.0438 D-ribose 5 105 150.0528 151.0600 149.0456 Guanine 5 5 5 1 151.0494 152.0566 150.0422Indole-3-acetotitrile Auxins IAN 10 7 2 155.0609 156.0681 154.0537Comments: Any molecule of known chemical composition can be added to thedatabase at any time. The database is comprised of accurate monoisotopicmasses. All molecules that have a unique empirical formula will have aunique accurate mass. This mass is a constant and is independent of themethodologies discussed herein making it possible to analyze all of thecomponents in a complex sample in a non-targeted fashion.

FIG. 2 shows two raw mass spectrums. The top one is from the extract ofa green stage strawberry and the lower one is from the extract of a redstage strawberry. Over 500 unique chemical entities were observed overthe mass range displayed above (100-350 amu; which is only a subset ofthe entire mass range analyzed (100-5000)). FIGS. 3, 4, and 5 showsmaller and smaller mass ranges to illustrate the separation of themetabolites.

FIG. 5 shows the resolution of the mass spectrum above 165,000. Thisextremely high resolution is necessary in order to separate all of themetabolites and thus be able to compare the two samples and determinethe changes, if any.

TABLE II Illustration of processed data (file ID, mass, intensity,empirical formula, relative error) FileID Mass Int C H N O P S Err C H NO P S Err ESI_POS_pri_4_rs2_50_50 99.044061 2.05E+06 5 6 0 2 0 0 0.05ESI_POS_pri_3_ts_50_50 99.044082 1.33E+06 5 6 0 2 0 0 0.26ESI_POS_pri_3_ts_50_50 102.054929 2.56E+06 4 7 1 2 0 0 0.25ESI_POS_pri_1_gs_50_50 102.054956 3.08E+06 4 7 1 2 0 0 0.01ESI_POS_pri_2_ws_50_50 102.054962 1.36E+06 4 7 1 2 0 0 0.07ESI_POS_pri_4_rs2_50_50 104.070595 1.93E+06 4 9 1 2 0 0 0.10ESI_POS_pri_4_rs1_50_50 104.070624 1.75E+06 4 9 1 2 0 0 0.18ESI_POS_pri_5_gs_acn 104.106977 2.73E+06 5 13 1 1 0 0 0.13ESI_POS_pri_2_ws_50_50 104.106979 2.73E+06 5 13 1 1 0 0 0.11ESI_POS_pri_6_ws_acn 104.106981 1.84E+06 5 13 1 1 0 0 0.09ESI_POS_pri_1_gs_50_50 104.107 3.88E+06 5 13 1 1 0 0 0.09ESI_POS_pri_3_ts_50_50 106.049869 1.21E+08 3 7 1 3 0 0 0.01ESI_POS_pri_1_gs_50_50 106.04987 1.36E+08 3 7 1 3 0 0 0.00ESI_POS_pri_2_ws_50_50 106.04987 1.63E+08 3 7 1 3 0 0 0.00ESI_POS_pri_4_rs1_50_50 106.04987 1.08E+08 3 7 1 3 0 0 0.00ESI_POS_pri_4_rs2_50_50 106.04987 1.53E+08 3 7 1 3 0 0 0.00ESI_POS_pri_5_gs_acn 106.04987 2.59E+08 3 7 1 3 0 0 0.00ESI_POS_pri_6_ws_acn 106.04987 2.45E+08 3 7 1 3 0 0 0.00ESI_POS_pri_7_ts_acn 106.04987 2.62E+08 3 7 1 3 0 0 0.00ESI_POS_pri_8_rs1_acn 106.04987 2.48E+08 3 7 1 3 0 0 0.00ESI_POS_pri_8_rs2_acn 106.04987 2.33E+08 3 7 1 3 0 0 0.00ESI_POS_pri_6_ws_acn 107.070237 1.34E+06 4 10 0 3 0 0 0.31ESI_POS_pri_8_rs1_acn 107.070322 1.28E+06 4 10 0 3 0 0 0.48ESI_POS_pri_7_ts_acn 108.080743 2.79E+06 7 9 1 0 0 0 0.30ESI_POS_pri_4_rs2_50_50 109.028414 1.65E+06 6 4 0 2 0 0 0.07ESI_POS_pri_4_rs2_50_50 111.044016 1.41E+06 6 6 0 2 0 0 0.36ESI_POS_pri_8_rs2_acn 114.091316 2.74E+06 6 11 1 1 0 0 0.21ESI_POS_pri_1_gs_50_50 114.091319 3.02E+06 6 11 1 1 0 0 0.19ESI_POS_pri_4_rs1_50_50 114.091336 1.76E+06 6 11 1 1 0 0 0.04ESI_POS_pri_5_gs_acn 114.091337 3.87E+06 6 11 1 1 0 0 0.03ESI_POS_pri_2_ws_50_50 114.091342 2.70E+06 6 11 1 1 0 0 0.01ESI_POS_pri_7_ts_acn 114.091346 3.26E+06 6 11 1 1 0 0 0.05ESI_POS_pri_6_ws_acn 114.091358 3.18E+06 6 11 1 1 0 0 0.15ESI_POS_pri_8_rs1_acn 114.091375 2.74E+06 6 11 1 1 0 0 0.30ESI_POS_pri_4_rs2_50_50 114.091377 2.53E+06 6 11 1 1 0 0 0.32ESI_POS_pri_3_ts_50_50 114.091404 2.21E+06 6 11 1 1 0 0 0.56ESI_POS_pri_4_rs2_50_50 115.038958 3.43E+06 5 6 0 3 0 0 0.11ESI_POS_pri_5_gs_acn 115.038978 2.03E+06 5 6 0 3 0 0 0.07ESI_POS_pri_2_ws_50_50 115.038984 1.84E+06 5 6 0 3 0 0 0.12ESI_POS_pri_8_rs1_acn 115.038999 1.57E+06 5 6 0 3 0 0 0.25ESI_POS_pri_4_rs1_50_50 115.039032 1.86E+06 5 6 0 3 0 0 0.53ESI_POS_pri_3_ts_50_50 115.03905 1.67E+06 5 6 0 3 0 0 0.69ESI_POS_pri_2_ws_50_50 116.034226 1.76E+06 4 5 1 3 0 0 0.06ESI_POS_pri_1_gs_50_50 116.034233 2.43E+06 4 5 1 3 0 0 0.12ESI_POS_pri_3_ts_50_50 116.03425 2.07E+06 4 5 1 3 0 0 0.26ESI_POS_pri_1_gs_50_50 116.070538 2.60E+06 5 9 1 2 0 0 0.58ESI_POS_pri_3_ts_50_50 116.070601 1.46E+06 5 9 1 2 0 0 0.03ESI_POS_pri_2_ws_50_50 116.070643 1.46E+06 5 9 1 2 0 0 0.33ESI_POS_pri_4_rs1_50_50 118.086184 1.56E+06 5 11 1 2 0 0 0.60ESI_POS_pri_1_gs_50_50 118.086217 4.10E+06 5 11 1 2 0 0 0.32ESI_POS_pri_4_rs2_50_50 118.086231 1.52E+06 5 11 1 2 0 0 0.20ESI_POS_pri_2_ws_50_50 118.086234 1.23E+06 5 11 1 2 0 0 0.18ESI_POS_pri_3_ts_50_50 118.086246 2.74E+06 5 11 1 2 0 0 0.08ESI_POS_pri_5_gs_acn 118.086249 2.53E+06 5 11 1 2 0 0 0.05 Comments: Themass spectrum is processed such that the 13C isotopes are firsteliminated (this is only possible in FTMS analysis due to the highresolution and mass accuracy).

Then the remaining peaks are automatically analyzed using the massanalysis program that is included with the instrument using specificconstraints chosen by the researcher (in the above example only thosepeaks that have the appropriate combination of carbon (C), hydrogen (H),oxygen (O), nitrogen (N), sulfur (S), or phosphorus (P) are returned).The final dataset now only contains monoisotopic, singly chargedmetabolites that have an accuracy of measurement of less than 1 ppm(err).

TABLE III Illustration of the database generated from the processeddata; Empirical Formula Green Stage White Stage Turning Stage C H N O PS Mass Int Mass Int WS/GS Mass Int 21 20 0 10 0 0 nf 1.30E+06 nf1.30E+06 100 433.1130 1.68E+07 25 34 6 19 0 0 nf 1.30E+06 723.19555.21E+07 4006 723.1952 1.12E+06 24 22 0 13 0 0 nf 1.30E+06 nf 1.30E+06100 519.1132 3.16E+06 22 32 6 1 0 0 nf 1.30E+06 nf 1.30E+06 100 nf1.30E+06 46 36 11 1 0 1 nf 1.30E+06 790.2621 2.62E+07 2015 790.26195.71E+07 19 17 11 3 0 0 nf 1.30E+06 448.1592 3.53E+07 2715 448.15914.88E+07 11 16 4 9 0 1 nf 1.30E+06 381.0710 1.68E+07 1292 381.07102.19E+07 9 18 8 5 0 3 nf 1.30E+06 nf 1.30E+06 100 nf 1.30E+06 30 67 19 40 0 nf 1.30E+06 nf 1.30E+06 100 756.5697 3.27E+07 47 71 7 3 0 0 nf1.30E+06 782.5697 3.67E+07 2623 782.5894 3.19E+07 22 40 14 5 0 2 nf1.30E+06 645.2625 2.27E+07 1746 645.2623 2.71E+07 23 24 8 5 0 1 nf1.30E+06 525.1667 4.15E+06 319 525.1683 1.54E+07 9 16 8 1 0 3 nf1.30E+06 nf 1.30E+06 100 349.0683 1.42E+06 20 28 4 11 0 1 nf 1.30E+06533.1550 5.75E+06 442 533.1551 1.54E+07 22 29 3 1 0 3 nf 1.30E+06448.1546 1.34E+07 1031 446.1545 1.73E+07 33 54 6 9 0 0 nf 1.30E+06879.4031 1.52E+07 1169 679.4025 1.58E+07 14 29 3 13 0 0 nf 1.30E+06448.1774 1.17E+07 900 448.1774 1.53E+07 15 20 0 11 0 0 nf 1.30E+06 nf1.30E+06 100 nf 1.30E+06 21 12 0 2 0 1 nf 1.30E+06 nf 1.30E+06 100 nf1.30E+06 40 34 8 0 0 3 nf 1.30E+06 nf 1.30E+06 100 nf 1.30E+06 27 50 2 50 2 nf 1.30E+06 547.3240 1.21E+07 931 547.3239 1.22E+07 21 44 2 21 0 2nf 1.30E+06 nf 1.30E+06 100 nf 1.30E+06 30 42 0 17 0 1 707.2222035.04E+06 707.2220 1.94E+07 385 707.2216 5.34E+07 12 24 4 11 0 1 nf1.30E+06 nf 1.30E+06 100 nf 1.30E+06 Empirical Formula Turning Stage RedStage C H N O P S TS/GS TS/WS Mass Int RS1/GS RS/WS RS/TS 21 20 0 10 0 01292 1292 433.1126 2.98E+06 22923 22923 1774 25 34 6 19 0 0 8615 215723.1953 1.41E+06 10846 271 126 24 22 0 13 0 0 243 243 513.1133 1.21E+069308 9308 3829 22 32 6 1 0 0 100 100 397.2714 6.32E+07 4862 4862 4862 4636 11 1 0 1 4392 218 790.2622 4.54E+07 3492 173 80 19 17 11 3 0 0 3754138 448.1592 4.02E+07 3092 114 82 11 16 4 9 0 1 1685 130 381.07092.75E+07 2115 164 126 9 18 8 5 0 3 100 100 415.0838 2.69E+07 2069 20692069 30 67 19 4 0 0 2515 2515 758.5696 2.44E+07 1877 1877 75 47 71 7 3 00 2454 87 782.5697 2.12E+07 1631 58 66 22 40 14 5 0 2 2085 119 645.28252.12E+07 1631 93 78 23 24 8 5 0 1 1185 371 525.1664 1.52E+07 1169 366 999 16 8 1 0 3 109 109 349.0685 1.50E+07 1154 1154 1056 20 28 4 11 0 11185 268 533.1550 1.38E+07 1062 240 90 22 29 3 1 0 3 1331 129 448.15481.32E+07 1015 99 76 33 54 6 9 0 0 1215 104 679.4028 1.31E+07 1006 88 8314 29 3 13 0 0 1177 131 448.1774 1.28E+07 985 109 84 15 20 0 11 0 0 100100 377.1078 1.24E+07 954 954 954 21 12 0 2 0 1 100 100 329.06341.17E+07 900 900 900 40 34 8 0 0 3 100 100 723.2143 1.13E+07 869 869 86927 50 2 5 0 2 938 101 647.3240 1.06E+07 815 88 87 21 44 2 21 0 2 100 100725.1951 1.05E+07 806 806 806 30 42 0 17 0 1 1060 275 707.2218 3.99E+07792 206 75 12 24 4 11 0 1 100 100 433.1235 9.92E+07 763 763 763Comments: In Table III, the data was sorted according to the relativeexpression of metabolites in the red stage vs the green stage ofstrawberry. The data can be organized by any field. What is observed isthat the metabolite C10H20O10 has a concentration that is at least22923% of that observed in the green stage (this metabolite is notobserved in the green stage so the value is a % of the backgroundnoise). This metabolite can be identified by its empirical formula aspelargonidin-3-glucoside, the primary pigment observed in strawberriesthat give them their red color. This process is automated.

TABLE IV Comparison of Metabolite and Gene Expression Data in StrawberryColor Formation (Red Stage vs. Green Stage) Relative Relative MetaboliteGene Metabolic Pathway Expression Expression 4-Coumarate-COA toNargingenin Chalcone  4.3 3.3 Naringenin Chalcone to Naringenin  4.3 4.3Leucopelargonidin to Pelargonidin 20* 6.7 Pelargonidin toPelargonidin-3-Glucoside 42* 8.3 *Reflects greater dynamic range ofmetabolic expression analysis Comments: FIGS. 7 through 12 and Table IVshow the power of non-targeted metabolic profiling in studying changesthat occur during development. Non-Targeted metabolic profiling allowsthe researcher to monitor entire metabolic pathways simultaneously.There is no other methodology that allows for the simultaneous analysisof such a diverse range of analytes. All of the analytes illustratedabove were extracted from the non-targeted data collected using themethodology and concepts presented in this application.and identification of unknown metabolites). Relative changes in3-Methylsulphinylheptyl Glucosinolate illustrated.

TABLE V Comparison of Glucosinolates in different Arabidopsis thalianamutants Arabidopsis Glucosinolate Mutants Glucosinolates R= WT TU1 TU3TU5 TU7 3-Methylthiobutyl 1.00 <0.06(nf) 2.69 0.14 0.363-Methylthiopentyl 1.00 <0.56(nf) 2.12 <0.56(nf) 0.71 3-Methylthioheptyl1.00 1.00 <0.21(nf) 0.32 <0.21(nf) 3-Methylthiooctyl 1.00 2.93 <0.09(nf)0.92 0.15 3-Methylsulphinylpropyl 1.00 27.62 1.37 21.56 0.373-Methylsulphinylbutyl 1.00 0.10 2.50 0.63 0.53 3-Methylsulphinylpentyl1.00 1.56 3.11 0.79 1.11 3-Methylsulphinylheptyl 1.00 1.38 <0.37(nf)0.64 <0.37(nf) 3-Methylsulphinyloctyl 1.00 6.16 <0.11(nf) 4.25 0.373-Indolylmethyl 1.00 4.44 0.90 1.85 0.71 Methoxy-3-Indolylmethyl 1.001.41 0.67 0.59 0.46 C3H7OS 1.00(nf) >6.88 nf nf nf C5H11O8S 1.00 2.680.73 0.85 0.60 C7H10OS3 1.00(nf) >5.73 nf >3.01 nf C8H12OS3 1.00<0.37(nf) 1.95 <0.37(nf) 0.45 C13H26NO3S 1.00 2.55 1.05 1.18 0.44C21H23O3 1.00 2.74 1.21 0.47 0.52 19 Glucosinolate Molecules Observed(17 reported) Comments: In Table V, the applicability of the technologyfor comparing genetic mutants to their wild-type counterparts isillustrated. The non-targeted metabolic profiles of four mutants (TU1,TU3, TU5, and TU7) were compared to their wild-type counterpart. Here weshow that not only can we identify and monitor the glucosinolates thathad been previously analyzed using targeted analysis, but were able toidentify previously unidentified glucosinolates. As is the case in allof our analyses, all of the other metabolites are also available forevaluation.

TABLE VI Illustration of database generated by directly comparing twosamples (carrot root exuate in the presence and absence of phosphate)Summary of Metabolites that were Observed to be Increased in the −PFraction −P/+P Minus P Plus P Proposed Empirical Formula Ratio (Corr.)Mode Mass Abs Int. Corr. Int. Mass Abs Int. C H N O 1172.550 ESI+245.0763 2.35E+09 1.17E+09 1.00E+06 10 9 8 2 1053.350 ESI+ 487.16722.11E+09 1.05E+09 1.00E+06 22 23 6 6 981.550 ESI+ 177.0546 1.96E+099.82E+08 1.00E+06 10 9 3 658.850 ESI+ 223.0965 1.32E+08 6.59E+081.00E+06 12 15 4 188.090 ESI+ 251.0524 3.72E+08 1.86E+08 1.00E+06 12 144 73.375 ESI+ 651.2412 1.47E+08 7.34E+07 1.00E+06 31 35 8 10 52.845 ESI+328.1390 1.08E+08 5.28E+07 1.00E+06 15 22 1 7 47.308 ESI+ 619.25099.46E+07 4.73E+07 1.00E+06 31 35 6 8 35.421 ESI+ 559.3239 7.08E+073.54E+07 1.00E+06 28 43 6 6 34.279 ESI+ 539.2813 6.86E+07 3.43E+071.00E+06 27 35 6 6 31.780 ESI+ 307.0489 8.38E+07 3.18E+07 1.00E+06 12 193 28.138 ESI+ 523.2299 5.63E+07 2.81E+07 1.00E+06 26 31 6 6 25.510 ESI+569.1988 5.10E+07 2.55E+07 1.00E+06 26 29 6 9 24.248 ESI− 279.12362.42E+07 2.42E+07 1.00E+06 15 19 5 22.393 ESI+ 535.3554 4.48E+072.24E+07 1.00E+06 34 47 8 8 21.312 ESI+ 543.3288 4.26E+07 2.13E+071.00E+06 28 43 8 5 20.003 APCI+ 377.1594 2.00E+07 2.00E+07 1.00E+06 2025 7 19.937 ESI+ 291.0714 3.99E+07 1.99E+07 1.00E+06 11 15 9 15.314APCI− 279.1239 1.53E+07 1.53E+07 1.00E+06 15 19 5 13.322 ESI+ 487.26632.66E+07 1.33E+07 1.00E+06 24 35 6 5 13.273 ESI− 335.2227 6.63E+076.63E+07 335.2227 5.00E+06 20 31 4 13.091 APCI− 335.2230 1.80E+081.60E+08 335.2231 1.22E+07 20 31 4 12.968 ESI+ 242.0700 2.59E+071.30E+07 1.00E+06 15 20 10 9 11.693 ESI+ 473.2507 2.34E+07 1.17E+071.00E+06 23 33 6 5 11.236 ESI− 167.6111 1.12E+07 1.12E+07 1.00E+06 18 293 3 9.001 ESI+ 149.0233 4.81E+08 2.40E+08 149.0233 2.67E+07 5 5 3 8.228ESI+ 459.2352 1.65E+07 8.23E+06 1.00E+06 22 31 6 5 8.011 APCI− 319.22673.59E+07 3.59E+07 319.2267 4.48E+06 20 31 3 7.742 ESI− 249.1494 2.14E+072.14E+07 249.1494 2.77E+06 16 21 3 7.279 ESI− 333.2071 1.43E+07 1.43E+07333.2071 1.98E+06 20 29 4 7.163 ESI+ 483.1415 1.43E+07 7.16E+06 1.00E+0624 28 8 6.902 ESI− 347.1864 1.15E+07 1.15E+07 347.1864 1.68E+6 20 27 56.655 APCI− 263.1290 5.86E+06 8.68E+08 1.00E+06 15 19 4 6.270 APCI−347.1867 1.87E+07 1.87E+07 347.1867 2.98E+06 20 27 5 6.019 ESI+ 345.12381.20E+07 6.02E+06 1.00E+06 14 22 6 5.306 ESI− 263.1287 5.31E+06 5.31E+061.00E+06 15 19 4 5.300 ESI+ 229.1047 1.06E+07 5.30E+06 1.00E+06 15 174.971 ESI− 191.1076 4.97E+06 4.97E+06 1.00E+06 12 15 2 4.603 ESI−213.1494 2.32E+07 2.32E+07 213.1494 5.03E+06 12 21 3 4.600 ESI− 277.14434.60E+06 4.60E+06 1.00E+06 16 21 4 4.524 APCI− 333.2074 2.20E+072.20E+07 333.2075 4.87E+06 20 29 4 4.163 ESI− 199.1341 1.18E+07 1.18E+07199.1341 2.83E+06 11 19 3 3.392 ESI− 227.1650 3.17E+07 3.17E+07 227.16509.33E+06 13 23 3 3.131 ESI+ 312.1441 6.26E+06 3.13E+06 1.00E+06 15 22 16 3.111 APCI− 249.1497 1.54E+07 1.54E+07 249.1497 4.95E+06 15 21 3 2.566APCI− 329.2336 2.29E+07 2.29E+07 329.2335 8.92E+06 13 33 5 2.438 ESI−415.1794 2.44E+06 2.44E+06 1.00E+06 20 31 7 2.017 ESI+ 285.0951 4.03E+062.02E+06 1.00E+06 10 17 6 −P/+P Proposed Empirical Formula ObservedTheoretical Error Ratio (Corr.) Mode P S Cl Na K a′ As Mass (ppm)1173.530 ESI+ −1 +H 245.07815 0.73 1053.350 ESI+ −1 +H 467.1673589 −0.45981.550 ESI+ −1 +H 177.0546208 −0.17 656.650 ESI+ −1 +H 223.0964854−0.16 188.090 ESI+ 1 −1 +K 261.0523672 0.05 73.375 ESI+ −1 +H651.2409178 0.48 52.845 ESI+ −1 +H 328.1390786 −0.24 47.308 ESI+ −1 +H619.2510885 −0.39 35.421 ESI+ −1 +H 559.3238596 0.13 34.279 ESI+ −1 +H539.2612593 0.00 31.780 ESI+ 3 −1 +H 307.049083 −0.60 28.136 ESI+ −1 +H523.2299592 −0.09 25.510 ESI+ −1 +H 569.199053 −0.44 24.248 ESI− 1 −H279.1237973 −0.60 22.393 ESI+ −1 +H 635.3551597 0.38 21.312 ESI+ −1 +H543.3269449 −0.21 20.003 APCI+ −1 +H 377.1594796 −0.18 19.937 ESI+ −1 +H291.0710585 1.04 15.314 APCI− 1 −H 279.1237973 0.26 13.322 ESI+ −1 +H487.2663447 −0.07 13.273 ESI− 1 −H 335.2227831 −0.40 13.091 APCI− 1 −H335.2227831 0.86 12.968 ESI+ −2 +2H 242.0701876 −0.86 11.693 ESI+ −1 +H473.2506946 0.10 11.236 ESI− 2 −2H 167.6109945 0.33 9.001 ESI+ −1 +H149.0233204 0.00 8.228 ESI+ −1 +H 459.2350446 0.36 8.011 APCI− −1 +H319.2267713 −0.22 7.742 ESI− 1 −H 249.1496181 −0.71 7.279 ESI− 1 −H333.207133 −0.13 7.163 ESI+ 1 −1 +K 483.1415762 −0.12 6.902 ESI− 1 −H347.1883976 −0.11 6.655 APCI− 1 −H 263.1288827 0.26 6.270 APCI− 1 −H347.1883976 0.83 6.019 ESI+ 1 1 −1 +K 345.1258237 −0.01 5.306 ESI− 1 −H263.1288827 −0.69 5.300 ESI+ 1 −1 +H 229.1045477 0.75 4.971 ESI− 1 −H191.1077533 −0.80 4.603 ESI− 1 −H 213.1496181 −1.02 4.600 ESI− 1 −H277.1445327 −0.64 4.524 APCI− 1 −H 333.207133 0.97 4.163 ESI− 1 −H199.1339681 0.61 3.392 ESI− 1 −H 227.1652682 −1.05 3.131 ESI+ −1 +H312.1441639 −0.08 3.111 APCI− 1 −H 249.1496181 0.19 2.566 APCI− 1 −H329.2333477 0.58 2.438 ESI− 1 1 −H 415.1795976 −0.60 2.017 ESI+ 2 −1 +H285.0950624 −0.01 Comments: Table VI illustrates how our technology canbe used to compare the metabolic profile of an organism under differentenvironmental conditions. Here we were able to detect and identify keymolecules involved in controlling the plant's response to phosphateconditions. This capability allows researchers to determine what effectschanges in environmental conditions will have on the biologicalfunctions of an organism.

TABLE VII MS/MS Data for Selected Metabolites Observed to be Increasedin the −P Fraction Parent Fragment Loss Of: C₃₁H₃₅N₆O₁₀ [H⁺]C₁₉H₂₃N₆O₅[H⁺] C₁₂H₁₂O₅ 651 C₁₉H₂₁N₆O₄[H⁺] C₁₂H₁₄O₆ +ESI *C₁₀H₉N₆O₂[H⁺]C₂₁H₂₄O₈ C₉H₇[H⁺] C₃₁H₃₅N₆O₈[H⁺] C₁₉H₂₃N₆O₅[H⁺] C₁₂H₁₂O₃ 619C₁₉H₂₁N₆O₄[H⁺] C₁₂H₁₄O₄ +ESI *C₁₀H₉N₆O₂[H⁺] C₂₁H₂₄O₆ C₉H₇[H⁺]C₂₆H₂₉N₆O₉[H⁺] C₁₉H₂₃N₆O₅[H⁺] C₇H₆O₄ 569 C₁₉H₂₁N₆O₄[H⁺] C₇H₈O₅ +ESI*C₁₀H₉N₆O₂[H⁺] C₁₆H₂₀O₇ C₉H₇[H⁺] C₂₈H₄₃N₆O₆[H⁺] C₁₉H₂₃N₆O₅[H⁺] C₉H₂₀O559 C₁₉H₂₁N₆O₄[H⁺] C₉H₂₂O₂ +ESI *C₁₀H₉N₆O₂[H⁺] C₁₈H₂₀O₄ C₉H₇[H⁺]C₂₈H₄₃N₆O₅[H⁺] C₁₉H₂₃N₆O₅[H⁺] C₉H₂₀ 543 C₁₉H₂₁N₆O₄[H⁺] C₉H₂₂O +ESI*C₁₀H₉N₆O₂[H⁺] C₁₈H₂₀O₃ C₉H₇[H⁺] C₂₇H₃₅N₆O₆[H⁺] C₁₉H₂₃N₆O₅[H⁺] C₈H₁₂O539 C₁₉H₂₁N₆O₄[H⁺] C₈H₁₄O₂ +ESI *C₁₅H₂₁N₆O₂[H⁺] *C₁₂H₁₄O₄ C₁₀H₉N₆O₂[H⁺]C₁₇H₂₆O₄ C₉H₇[H⁺] C₂₆H₃₁N₆O₆[H⁺] C₁₉H₂₃N₆O₅[H⁺] C₇H₉O 523 C₁₉H₂₁N₆O₄[H⁺]C₇H₁₀O₂ +ESI *C₁₄H₁₇N₆O₂[H⁺] *C₁₂H₁₄O₄ C₁₀H₉N₆O₂[H⁺] C₁₆H₂₂O₄ C₉H₇[H⁺]C₂₂H₂₃N₆O₆[H⁺] *C₁₀H₉N₆O₂[H⁺] *C₁₂H₁₄O₄ 467 +ESI *C₁₂H₁₅O₄[H⁺]*C₁₀H₉O₃[H⁺] C₂H₆O 223 C₉H₇O₃[H⁺] C₃H₈O +ESI C₈H₅O₃[H⁺] C₄H₁₀O C₆H₅O[H⁺]C₆H₁₀O₃ *C₁₀H₉O₃[H⁺] *C₈H₅O₃[H⁺] C₂H₄ 177 C₆H₅O[H⁺] C₄H₄O₂ +ESI*C₈H₅O₃[H⁺] C₇H₅O₂[H⁺] CO 149 C₆H₅O[H⁺] C₂O₂ +ESI

TABLE VIII Determination of Metabolite Relations using MS/MS data R1 R3R2 C₁₀H₈N₆O₂ None C₁₂H₁₄O₄ C₁₀H₈N₆O₂ C₄H₈ C₁₂H₁₄O₄ C₁₀H₈N₆O₂ C₅H₁₂C₁₂H₁₄O₄ C₁₀H₈N₆O₂ C₆H₆ C₁₂H₁₄O₄ C₁₀H₈N₆O₂ C₄H₆O₃ C₁₂H₁₄O₄ C₁₀H₈N₆O₂C₉H₁₀O₂ C₁₂H₁₄O₄ C₁₀H₈N₆O₂ C₉H₁₀O₄ C₁₂H₁₄O₄ C₁₀H₈N₆O₂ C₆H₆ C₁₂H₁₄O₃

TABLE IX Mass Analysis of unknown peak observed in Tobacco FlowerAnalysis Mass Analysis of Unknown Peak Calibration Constants: ML1:108299134.679450 ML2: −16.576817 ML3: −2029.796744 Calibration Results:Ref. Masses Exp. Masses Diff (ppm) 124.039300 124.039298 0.0187161.092070 161.092079 0.0542 303.166300 303.166272 0.0919 609.280660609.280664 0.0060 962.430130 962.430230 0.1037 Observed Mass of Unknown:595.16572 Empirical Formula Search Result: C₂₇H₃₀O₁₅ [+H]+ Mass:595.16575 Mass Error: 0.04 ppm Proposed Metabolite: C₁₅H₁₀O₆ -Rhamnoglucoside (present in flowers of grapefruit) Comments: FIGS. 16and 17 and Table IX show how our technology provides meaningfulinformation that would otherwise not be obtained. In this example theresearcher thought that he knew the primary color component in tobaccoflowers (C15H10O6-Glucoside) but our analysis showed that the primarycolor component in tobacco flowers is actually the rhamnoglucoside. Thisillustrates the power of being able to identify unknown components afteranalysis. No other technology is currently available to provide thistype of analysis.

TABLE X Illustration of the number of metabolites monitored instrawberry extracts. Summary of Metabolites Observed from DifferentExtraction Methods and Ionization Conditions. Number of UniqueMetabolites Observed 50/50 ACN In Both Total ESI+ 1143 1054 540 1657ESI− 966 790 211 1545 APCI+ 979 1431 615 1795 APCI− 898 1205 370 1733Total 3986 4480 1736 6730

Table X and FIG. 18 illustrate the comprehensive nature of ourinvention. Our technology allows for the comprehensive comparison of themetabolic profiles of organisms under varying environmental, genetic,and developmental conditions.

In this patent document, the word “comprising” is used in itsnon-limiting sense to mean that items following the word are included,but items not specifically mentioned are not excluded A reference to anelement by the indefinite article “a” does not exclude the possibilitythat more than one of the element is present, unless the context clearlyrequires that there be one and only one of the elements.

It will be apparent to one skilled in the art that modifications may bemade to the illustrated embodiment without departing from the spirit andscope of the invention as hereinafter defined in the Claims.

1. A method for analysis of a plurality of biological samples toidentify one or more unidentified metabolites of different intensitiesbetween samples that are associated with a human disease state,comprising the steps of: a) introducing the plurality of biologicalsamples from a healthy human and a diseased human, each of whichcontains a plurality of unidentified metabolites without any a prioriselection of metabolites of interest, into a Fourier Transform IonCyclotron Resonance Mass Spectrometer (FTMS); b) simultaneouslyobtaining, identifying and quantifying data for the plurality ofunidentified metabolites detected in each of the biological samplesintroduced into the FTMS, wherein the identifying data comprise accuratemass and the quantifying data are intensity data; c) creating a databasecomprising said identifying and quantifying data; d) analyzing thedatabase to determine metabolites of different intensities betweensamples from the healthy human and the diseased human, which metabolitesof different intensities are associated with the human disease state; e)identifying one or more unidentified metabolites associated with thehuman disease state so determined by a method selected from the groupconsisting of matching the identifying data of the unidentifiedmetabolites to identifying data of known metabolites, determining theempirical formula of the one or more unidentified metabolites, andanalyzing the MS/MS fragment data of the one or more unidentifiedmetabolites.
 2. The method as defined in claim 1 further comprising:correlating the one or more unidentified metabolites associated with thehuman disease state identified in step e) with gene expression data fromthe healthy human and/or diseased human to determine the function of oneor more genes affected by the human disease state.
 3. The method asdefined in claim 1, wherein each of the biological samples is abiological extract of metabolites.
 4. The method as defined in claim 1,wherein the accurate mass is used to calculate the empirical formula ofthe one or more than one unidentified metabolites.
 5. The method asdefined in claim 4, wherein the database created in step c) of claim 1is organized to permit searching for one or more known metabolites byempirical formula.
 6. The method as defined in claim 1, wherein thedatabase created in step c) of claim 1 is organized to permit searchingfor one or more known metabolites by accurate mass.
 7. The method asdefined in claim 4, wherein the database created in step c) of claim 1is organized to permit identification of unknown metabolites by theempirical formulae of the metabolites.
 8. The method as defined in claim1, wherein the database created in step c) of claim 1 is organized topermit the comparison of one or more samples from diseased humans to oneor more samples from healthy humans and/or samples from differentdiseased humans such that the intensity of metabolites present in thesamples from diseased humans can be determined relative to the samplesfrom healthy humans and/or samples from different diseased humans. 9.The method as defined in claim 1, wherein the FTMS is used with achromatographic separation system.
 10. The method as defined in claim 1,wherein the FTMS is equipped with a soft ionization source.
 11. Themethod as defined in claim 1, wherein the FTMS is equipped with anadditional mass selective pre-separation system.
 12. The method asdefined in claim 1, wherein the database created in step c) of claim 1is organized to permit the comparison of any two or more samples to eachother, such that the presence or absence of an intensity of metabolitesfound in some samples but not in others is determined.
 13. The method asdefined in claim 1, wherein the database created in step c) of claim 1is organized to permit the comparison of one or more samples fromdiseased humans to one or more samples from healthy humans and/orsamples from different diseased humans such that the presence or absenceof an intensity of metabolites present in the samples from diseasedhumans can be determined relative to the samples from healthy humansand/or samples from different diseased humans.
 14. A method for theanalysis of a plurality of biological samples to identify one or moreunidentified metabolites of different intensities between samples thatare associated with a human disease state, comprising the steps of: a)injecting a plurality of biological samples from a healthy human and adiseased human, each of which contains a plurality of unidentifiedmetabolites without any a priori selection of metabolites of interest,into a Fourier Transform Ion Cyclotron Mass Spectrometer with or withoutthe additional use of a chromatographic column; b) ionizing themetabolites using a soft ionization source; c) transferring the ionizedmetabolites to an ion cyclotron resonance (ICR) cell with or withoutadditional mass selective pre-separation; d) separating and measuringsaid ions in the ICR cell with or without simultaneous MS/MS analysisoccurring; e) simultaneously determining accurate mass and intensitydata of each of the ions detected; f) transferring said data to adatabase that stores and organizes the data; g) comparing intensity dataof the biological samples from the healthy human and the diseased humancontained within the database to one another to determine metabolites ofdifferent intensities as between samples, which metabolites of differentintensities are associated with the human disease state; and h)identifying one or more unidentified metabolites so determined, by amethod selected from the group consisting of matching the accurate massdata of the unidentified metabolites to accurate mass data of knownmetabolites, calculating the empirical formula of the one or moreunidentified metabolites, and analyzing the MS/MS fragment data of theone or more unidentified metabolites.
 15. The method as defined in claim14 further comprising: correlating the one or more unidentifiedmetabolites associated with the human disease state identified in steph) with gene expression data from the healthy human and/or diseasedhuman to determine the function of one or more genes affected by thehuman disease state.
 16. A method for analysis of a plurality ofbiological samples to identify one or more unidentified metabolites fromsaid samples that are associated with a human disease state, whencompared with a database of known metabolites, comprising the steps of:a) introducing a plurality of biological samples from a healthy humanand a diseased human, each of which contains a plurality of unidentifiedmetabolites without any a priori selection of metabolites of interest,into a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer(FTMS); b) simultaneously obtaining, identifying and quantifying datafor the plurality of unidentified metabolites detected in each of thebiological samples introduced into the FTMS, wherein the identifyingdata comprise accurate mass data and the quantifying data are intensitydata; c) creating a database comprising said identifying and quantifyingdata; d) comparing the identifying and quantifying data of the saiddatabase with a known database containing identifying and quantifyingdata of known metabolites; and e) identifying one or more unidentifiedmetabolites so compared by matching said identifying data ofunidentified metabolites to said identifying data of known metabolites;and f) comparing intensity data of the biological samples from thehealthy human and the diseased human for the metabolites identified instep e) to identify metabolites that are associated with the humandisease state.
 17. A method for analysis of a plurality of biologicalsamples to create and organize a database, by metabolic concentration,to indicate unidentified metabolites associated with a human diseasestate comprising the steps of: a) introducing a plurality of biologicalsamples from a healthy human and a diseased human, each of whichcontains a plurality of unidentified metabolites without any a prioriselection of metabolites of interest, into a Fourier Transform IonCyclotron Resonance Mass Spectrometer (FTMS); b) simultaneouslyobtaining, identifying and quantifying data for the plurality ofunidentified metabolites detected in each of the biological samplesintroduced into the FTMS, wherein the identifying data comprise accuratemass data and the quantifying data are intensity data; c) creating adatabase comprising said identifying and quantifying data; and d)organizing the database by metabolic concentration whereby theorganization of said identifying and quantifying data indicates which ofthe unidentified metabolites is associated with the human disease state.18. A method for analysis of a plurality of biological samples,comprising the steps of: a) introducing a plurality of biologicalsamples from a healthy human and a diseased human, each of whichcontains a plurality of unidentified metabolites without any a prioriselection of metabolites of interest, into a Fourier Transform IonCyclotron Resonance Mass Spectrometer (FTMS); b) simultaneouslyobtaining, identifying and quantifying data for the plurality ofunidentified metabolites detected in each of the biological samplesintroduced into the FTMS, wherein the identifying data comprise accuratemass data and the quantifying data are intensity data; c) creating adatabase of unidentified metabolites comprising said identifying andquantifying data; and d) analyzing the database of unidentifiedmetabolites, wherein the analyzing step is selected from the groupconsisting of: (i) analyzing the database of unidentified metabolites todetermine metabolites of different intensities between samples from thehealthy human and the diseased human, which metabolites of differentintensities are associated with a human disease state, and identifyingone or more metabolites so determined using the identifying data bymatching to a known database of known metabolites, or by the empiricalformula of the one or more than one unidentified metabolites, or by theMS/MS fragment data of the one or more than one unidentifiedmetabolites; (ii) comparing the database of the unidentified metaboliteswith a known database of known metabolites and identifying one or moremetabolites associated with the human disease state so compared usingthe identifying data and matching said identifying data to said knowndatabase of known metabolites; (iii) organizing the database of theunidentified metabolites by metabolite concentration; and (iv)correlating the unidentified metabolites in (iii) with gene expressiondata from the healthy and diseased humans to determine the function ofone or more genes affected by the human disease state.